Post-GDPR AI: Federated Audit Trails and Compliance Automation for Data Engineering

Implementation of the General Data Protection Regulation (GDPR) has required a paradigm change in the gathering, processing and management of data across decentralized systems. Conventional centralized co-productions of compliance processes fail to hold up in providing accountability and transparency to the AI-driven data engineering workflow practices. The current paper envisions a new framework that combines federated audit trails, compliance automation based on AI to withstand the regulatory challenges presented by the GDPR after it became law. By applying the principles of federated learning, the proposed architecture also decentralizes audit log generation and verification without reducing data privacy creating provenance of data and consent tracking of users in the context of multicloud settings. In addition, smart policy engines powered by machine learning will automate compliance checks in real-time and identify violations as they occur on an ongoing basis and adapt to new legal standards. Experimental tests on both synthetic and real-world data have shown large gains in the regulatory traceability, scalability, and audit efficiency of a system relative to conventional logging mechanisms. This paper sets the stage towards privacy-respecting, explainable, and legally-accountable artificial intelligence infrastructures in the age of data protection around the world.